The Revenue Mindset

Introduction: Why Analysts Need a Revenue Mindset

“The purpose of a firm is to maximize the wealth of its owners.”
— Michael Jensen, Harvard Business School professor

As a data analyst, your role sits at the intersection of business and technology.

We engage with the technology side through data — using tools like SQL, Python, and spreadsheets to explore, measure, and report. But when it comes to the business side, our job is to help stakeholders — product managers, business leads, operations teams, and others — answer the questions that matter to them.

And what matters to them, more than anything else?

Showing results. Hitting goals. Driving growth.
In other words: making money.

That’s what the revenue mindset is all about.

It means understanding how the business grows, where the money comes from, and what levers actually move it. Analysts who think this way ask better questions, prioritize what really matters, and deliver insights leadership cares about.

You don’t need a business degree to get there.
You just need a few core principles — and to learn how they show up in the real world.

Revenue vs. Profit vs. ROI

Let’s start by getting the financial basics straight. These three terms — revenue, profit, and ROI — come up constantly in business, and while they’re often lumped together, they each tell a different part of the story.

Revenue

Definition: The total money a company earns from selling its products or services.

Example:
An eCommerce dog food store sells 500 bags at $100 each.
Revenue = 500 × $100 = $50,000

Revenue is often called the top line — it shows how much money came in, but not how much the business keeps.

Profit

Definition: What’s left after subtracting all costs from revenue.

Example:
If it costs $30,000 to purchase, market, and deliver the products:
Profit = $50,000 – $30,000 = $20,000

This is the bottom line — it tells you whether the business actually made money after expenses.

A company can have high revenue and still lose money if costs are too high.

ROI (Return on Investment)

Definition: A way to evaluate how efficiently a company turns investments into profit.
ROI is calculated this way:
ROI = (Gain from Investment – Cost of Investment) ÷ Cost of Investment

Example:
A company spends $5,000 on a campaign that brings in $15,000 in revenue:
ROI = (15,000 – 5,000) ÷ 5,000 = 200%

That means for every dollar spent, the company earned two in return.

ROI is a metric — and an important one. We’ll dig deeper into metrics later in the course.

What Counts as an “Investment”?

An investment is any use of company resources with the expectation of generating value. This includes:

A paid ad campaign
Developing a new feature
Hiring new staff
Offering customer discounts
Running a promotional event

It’s not just about money — it’s about allocating resources (time, money, people) to drive results.

How Companies Grow Revenue

Most companies grow revenue in one or more of the following ways:

1. Getting More Customers (Customer Acquisition)

Bringing in new people who’ve never bought from the company before.

Owned by marketing/sales teams through ads, outreach, partnerships
Example: A data course runs Instagram ads to get more signups

2. Getting Customers to Buy More (Increasing Purchase Volume)

Encouraging more frequent purchases or bigger orders.

Example: A shopper who used to buy 1 book a month now buys 3 with a “Buy 2, Get 1” promo

3. Selling New Things (Product or Market Expansion)

Launching new products or targeting new segments.

Example: A food delivery app adds groceries to its restaurant service

4. Keeping Customers Longer (Retention)

Reducing churn and increasing lifetime value.

Example: A fitness app improves onboarding so more users stick around

5. Raising Prices (Pricing Power)

Charging more without losing customers.

Example: Netflix increases its monthly price and retains most users

How Companies Make Money: Business Models for Dummies

Every company has a business model — a way of generating revenue.
Knowing the model helps you ask smarter questions and focus on the right metrics.

Business Model	What It Sells	How It Makes Money	Example Companies
eCommerce	Physical or digital products	Sells items directly to customers	Amazon, Etsy
Subscription (SaaS)	Software access	Monthly or annual subscription fees	Notion, Dropbox
Subscription (Content)	Digital media/content	Recurring access fees	Netflix, NYTimes
Marketplace	Buyer/seller connections	Takes a cut from each transaction	Airbnb, eBay
Services	Time, expertise	Charges per hour, project, or deliverable	Deloitte
Advertising-based	User attention	Earns revenue from ads shown to users	YouTub

Some companies use multiple models (e.g., YouTube has both ads and subscriptions).

Different models = different success metrics.
As an analyst, you need to know which model you’re working with.

Revenue Doesn’t Come from Just One Team

It’s easy to assume that revenue is sales’ job — but in reality, every team contributes.

Here’s how:

Marketing brings in leads
Sales closes them
Product improves UX → better retention and upsells
Customer Support reduces churn
Operations improves efficiency and margins

Each of these teams affects revenue. And each relies on data to do it better.

Conclusion: The Analyst as a Business Partner

When you understand how your company makes money, you start to see the bigger picture.

You spot which problems are worth solving
You ask sharper, more impactful questions
You make recommendations that actually move the needle

That’s the revenue mindset.
It’s not about becoming a finance expert — it’s about connecting your work to value.

Key Terms Recap

Revenue – Total income from sales
Profit – Revenue minus costs
ROI – Return on any investment, expressed as a %
Churn – % of users/customers who left
Conversion Rate – % of users who completed a desired action
Retention – % of users who stayed active or returned
Business Model – How the company earns revenue
Upsell – Getting users to upgrade or buy more

Up Next → 1.2 What Customers Care About

If revenue is the outcome, customer value is the source.

You can’t grow a business without understanding what people actually want — and what they’re willing to pay for.

In the next chapter, we’ll explore:

What customers truly care about (and what they don’t)
Why great analysts think with empathy, not just data
How understanding customer motivations leads to better analysis and smarter recommendations

Let’s keep going.